MinMaxScaler¶

path: sklearn.preprocessing.MinMaxScaler

description: this primitive transforms features by scaling each feature to a given range.

argument	type	description
parameters
`X`	`numpy.ndarray`	the data used to compute the per-feature minimum and maximum used for later scaling along the features axis
hyperparameters
`feature_range`	`tuple`	desired range of transformed data. Default set to `[0, 1]`
`copy`	`bool`	if True, a copy of X will be created
output
`X`	`numpy.ndarray`	a transformed version of X

In [1]: import numpy as np

In [2]: from mlstars import load_primitive

In [3]: X = np.array(range(5)).reshape(-1, 1)

In [4]: primitive = load_primitive('sklearn.preprocessing.MinMaxScaler',
   ...:     arguments={"X": X, "feature_range":[0, 1]})
   ...: 

In [5]: primitive.fit()

In [6]: primitive.produce(X=X)
Out[6]: 
array([[0.  ],
       [0.25],
       [0.5 ],
       [0.75],
       [1.  ]])

SimpleImputer ARIMA